What is the impact of scaling the features on the performance of the model?
In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations. We do feature scaling to make our model robust to outliers and make an initial impact of every feature on the model will be roughly similar

Graphical-model based classifiers, such as Fisher LDA or Naive Bayes, as well as Decision trees and Tree-based ensemble methods (RF, XGB) are invariant to feature scaling, but, still, it might be a good idea to rescale/standardize your data.


You should explore your data more carefully, find the outliers, apply transformation if needed.
Not sure if it is a good idea
You can apply different preprocessing techniques like MinMaxScaller, Rank,  Log transform, Extracting square root, StandartScaller and etc.